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diff --git a/doc/py_tutorials/py_objdetect/py_face_detection/py_face_detection.markdown b/doc/py_tutorials/py_objdetect/py_face_detection/py_face_detection.markdown
index 3b4308a958..81973190aa 100644
--- a/doc/py_tutorials/py_objdetect/py_face_detection/py_face_detection.markdown
+++ b/doc/py_tutorials/py_objdetect/py_face_detection/py_face_detection.markdown
@@ -1,134 +1,4 @@
Face Detection using Haar Cascades {#tutorial_py_face_detection}
==================================
-Goal
-----
-
-In this session,
-
-- We will see the basics of face detection using Haar Feature-based Cascade Classifiers
-- We will extend the same for eye detection etc.
-
-Basics
-------
-
-Object Detection using Haar feature-based cascade classifiers is an effective object detection
-method proposed by Paul Viola and Michael Jones in their paper, "Rapid Object Detection using a
-Boosted Cascade of Simple Features" in 2001. It is a machine learning based approach where a cascade
-function is trained from a lot of positive and negative images. It is then used to detect objects in
-other images.
-
-Here we will work with face detection. Initially, the algorithm needs a lot of positive images
-(images of faces) and negative images (images without faces) to train the classifier. Then we need
-to extract features from it. For this, Haar features shown in the below image are used. They are just
-like our convolutional kernel. Each feature is a single value obtained by subtracting sum of pixels
-under the white rectangle from sum of pixels under the black rectangle.
-
-
-
-Now, all possible sizes and locations of each kernel are used to calculate lots of features. (Just
-imagine how much computation it needs? Even a 24x24 window results over 160000 features). For each
-feature calculation, we need to find the sum of the pixels under white and black rectangles. To solve
-this, they introduced the integral image. However large your image, it reduces the calculations for a
-given pixel to an operation involving just four pixels. Nice, isn't it? It makes things super-fast.
-
-But among all these features we calculated, most of them are irrelevant. For example, consider the
-image below. The top row shows two good features. The first feature selected seems to focus on the
-property that the region of the eyes is often darker than the region of the nose and cheeks. The
-second feature selected relies on the property that the eyes are darker than the bridge of the nose.
-But the same windows applied to cheeks or any other place is irrelevant. So how do we select the
-best features out of 160000+ features? It is achieved by **Adaboost**.
-
-
-
-For this, we apply each and every feature on all the training images. For each feature, it finds the
-best threshold which will classify the faces to positive and negative. Obviously, there will be
-errors or misclassifications. We select the features with minimum error rate, which means they are
-the features that most accurately classify the face and non-face images. (The process is not as simple as
-this. Each image is given an equal weight in the beginning. After each classification, weights of
-misclassified images are increased. Then the same process is done. New error rates are calculated.
-Also new weights. The process is continued until the required accuracy or error rate is achieved or
-the required number of features are found).
-
-The final classifier is a weighted sum of these weak classifiers. It is called weak because it alone
-can't classify the image, but together with others forms a strong classifier. The paper says even
-200 features provide detection with 95% accuracy. Their final setup had around 6000 features.
-(Imagine a reduction from 160000+ features to 6000 features. That is a big gain).
-
-So now you take an image. Take each 24x24 window. Apply 6000 features to it. Check if it is face or
-not. Wow.. Isn't it a little inefficient and time consuming? Yes, it is. The authors have a good
-solution for that.
-
-In an image, most of the image is non-face region. So it is a better idea to have a simple
-method to check if a window is not a face region. If it is not, discard it in a single shot, and don't
-process it again. Instead, focus on regions where there can be a face. This way, we spend more time
-checking possible face regions.
-
-For this they introduced the concept of **Cascade of Classifiers**. Instead of applying all 6000
-features on a window, the features are grouped into different stages of classifiers and applied one-by-one.
-(Normally the first few stages will contain very many fewer features). If a window fails the first
-stage, discard it. We don't consider the remaining features on it. If it passes, apply the second stage
-of features and continue the process. The window which passes all stages is a face region. How is
-that plan!
-
-The authors' detector had 6000+ features with 38 stages with 1, 10, 25, 25 and 50 features in the first five
-stages. (The two features in the above image are actually obtained as the best two features from
-Adaboost). According to the authors, on average 10 features out of 6000+ are evaluated per
-sub-window.
-
-So this is a simple intuitive explanation of how Viola-Jones face detection works. Read the paper for
-more details or check out the references in the Additional Resources section.
-
-Haar-cascade Detection in OpenCV
---------------------------------
-
-OpenCV comes with a trainer as well as detector. If you want to train your own classifier for any
-object like car, planes etc. you can use OpenCV to create one. Its full details are given here:
-[Cascade Classifier Training](@ref tutorial_traincascade).
-
-Here we will deal with detection. OpenCV already contains many pre-trained classifiers for face,
-eyes, smiles, etc. Those XML files are stored in the opencv/data/haarcascades/ folder. Let's create a
-face and eye detector with OpenCV.
-
-First we need to load the required XML classifiers. Then load our input image (or video) in
-grayscale mode.
-@code{.py}
-import numpy as np
-import cv2 as cv
-
-face_cascade = cv.CascadeClassifier('haarcascade_frontalface_default.xml')
-eye_cascade = cv.CascadeClassifier('haarcascade_eye.xml')
-
-img = cv.imread('sachin.jpg')
-gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
-@endcode
-Now we find the faces in the image. If faces are found, it returns the positions of detected faces
-as Rect(x,y,w,h). Once we get these locations, we can create a ROI for the face and apply eye
-detection on this ROI (since eyes are always on the face !!! ).
-@code{.py}
-faces = face_cascade.detectMultiScale(gray, 1.3, 5)
-for (x,y,w,h) in faces:
- cv.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
- roi_gray = gray[y:y+h, x:x+w]
- roi_color = img[y:y+h, x:x+w]
- eyes = eye_cascade.detectMultiScale(roi_gray)
- for (ex,ey,ew,eh) in eyes:
- cv.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(0,255,0),2)
-
-cv.imshow('img',img)
-cv.waitKey(0)
-cv.destroyAllWindows()
-@endcode
-Result looks like below:
-
-
-
-Additional Resources
---------------------
-
--# Video Lecture on [Face Detection and Tracking](https://www.youtube.com/watch?v=WfdYYNamHZ8)
--# An interesting interview regarding Face Detection by [Adam
- Harvey](https://web.archive.org/web/20171204220159/http://www.makematics.com/research/viola-jones/)
-
-Exercises
----------
+Tutorial content has been moved: @ref tutorial_cascade_classifier
diff --git a/doc/py_tutorials/py_objdetect/py_table_of_contents_objdetect.markdown b/doc/py_tutorials/py_objdetect/py_table_of_contents_objdetect.markdown
index 8ea0504ea4..9375ca1e4b 100644
--- a/doc/py_tutorials/py_objdetect/py_table_of_contents_objdetect.markdown
+++ b/doc/py_tutorials/py_objdetect/py_table_of_contents_objdetect.markdown
@@ -1,7 +1,4 @@
Object Detection {#tutorial_py_table_of_contents_objdetect}
================
-- @subpage tutorial_py_face_detection
-
- Face detection
- using haar-cascades
+Content has been moved: @ref tutorial_table_of_content_objdetect
diff --git a/doc/py_tutorials/py_tutorials.markdown b/doc/py_tutorials/py_tutorials.markdown
index 7d9298d68e..6957cac53d 100644
--- a/doc/py_tutorials/py_tutorials.markdown
+++ b/doc/py_tutorials/py_tutorials.markdown
@@ -45,10 +45,10 @@ OpenCV-Python Tutorials {#tutorial_py_root}
In this section you
will learn different computational photography techniques like image denoising etc.
-- @subpage tutorial_py_table_of_contents_objdetect
+- @ref tutorial_table_of_content_objdetect
In this section you
- will object detection techniques like face detection etc.
+ will learn object detection techniques like face detection etc.
- @subpage tutorial_py_table_of_contents_bindings
diff --git a/doc/tutorials/objdetect/cascade_classifier/cascade_classifier.markdown b/doc/tutorials/objdetect/cascade_classifier/cascade_classifier.markdown
index 3c7bf6b90c..27a506ff82 100644
--- a/doc/tutorials/objdetect/cascade_classifier/cascade_classifier.markdown
+++ b/doc/tutorials/objdetect/cascade_classifier/cascade_classifier.markdown
@@ -4,9 +4,11 @@ Cascade Classifier {#tutorial_cascade_classifier}
Goal
----
-In this tutorial you will learn how to:
+In this tutorial,
-- Use the @ref cv::CascadeClassifier class to detect objects in a video stream. Particularly, we
+- We will learn how the Haar cascade object detection works.
+- We will see the basics of face detection and eye detection using the Haar Feature-based Cascade Classifiers
+- We will use the @ref cv::CascadeClassifier class to detect objects in a video stream. Particularly, we
will use the functions:
- @ref cv::CascadeClassifier::load to load a .xml classifier file. It can be either a Haar or a LBP classifer
- @ref cv::CascadeClassifier::detectMultiScale to perform the detection.
@@ -14,8 +16,81 @@ In this tutorial you will learn how to:
Theory
------
-Code
-----
+Object Detection using Haar feature-based cascade classifiers is an effective object detection
+method proposed by Paul Viola and Michael Jones in their paper, "Rapid Object Detection using a
+Boosted Cascade of Simple Features" in 2001. It is a machine learning based approach where a cascade
+function is trained from a lot of positive and negative images. It is then used to detect objects in
+other images.
+
+Here we will work with face detection. Initially, the algorithm needs a lot of positive images
+(images of faces) and negative images (images without faces) to train the classifier. Then we need
+to extract features from it. For this, Haar features shown in the below image are used. They are just
+like our convolutional kernel. Each feature is a single value obtained by subtracting sum of pixels
+under the white rectangle from sum of pixels under the black rectangle.
+
+
+
+Now, all possible sizes and locations of each kernel are used to calculate lots of features. (Just
+imagine how much computation it needs? Even a 24x24 window results over 160000 features). For each
+feature calculation, we need to find the sum of the pixels under white and black rectangles. To solve
+this, they introduced the integral image. However large your image, it reduces the calculations for a
+given pixel to an operation involving just four pixels. Nice, isn't it? It makes things super-fast.
+
+But among all these features we calculated, most of them are irrelevant. For example, consider the
+image below. The top row shows two good features. The first feature selected seems to focus on the
+property that the region of the eyes is often darker than the region of the nose and cheeks. The
+second feature selected relies on the property that the eyes are darker than the bridge of the nose.
+But the same windows applied to cheeks or any other place is irrelevant. So how do we select the
+best features out of 160000+ features? It is achieved by **Adaboost**.
+
+
+
+For this, we apply each and every feature on all the training images. For each feature, it finds the
+best threshold which will classify the faces to positive and negative. Obviously, there will be
+errors or misclassifications. We select the features with minimum error rate, which means they are
+the features that most accurately classify the face and non-face images. (The process is not as simple as
+this. Each image is given an equal weight in the beginning. After each classification, weights of
+misclassified images are increased. Then the same process is done. New error rates are calculated.
+Also new weights. The process is continued until the required accuracy or error rate is achieved or
+the required number of features are found).
+
+The final classifier is a weighted sum of these weak classifiers. It is called weak because it alone
+can't classify the image, but together with others forms a strong classifier. The paper says even
+200 features provide detection with 95% accuracy. Their final setup had around 6000 features.
+(Imagine a reduction from 160000+ features to 6000 features. That is a big gain).
+
+So now you take an image. Take each 24x24 window. Apply 6000 features to it. Check if it is face or
+not. Wow.. Isn't it a little inefficient and time consuming? Yes, it is. The authors have a good
+solution for that.
+
+In an image, most of the image is non-face region. So it is a better idea to have a simple
+method to check if a window is not a face region. If it is not, discard it in a single shot, and don't
+process it again. Instead, focus on regions where there can be a face. This way, we spend more time
+checking possible face regions.
+
+For this they introduced the concept of **Cascade of Classifiers**. Instead of applying all 6000
+features on a window, the features are grouped into different stages of classifiers and applied one-by-one.
+(Normally the first few stages will contain very many fewer features). If a window fails the first
+stage, discard it. We don't consider the remaining features on it. If it passes, apply the second stage
+of features and continue the process. The window which passes all stages is a face region. How is
+that plan!
+
+The authors' detector had 6000+ features with 38 stages with 1, 10, 25, 25 and 50 features in the first five
+stages. (The two features in the above image are actually obtained as the best two features from
+Adaboost). According to the authors, on average 10 features out of 6000+ are evaluated per
+sub-window.
+
+So this is a simple intuitive explanation of how Viola-Jones face detection works. Read the paper for
+more details or check out the references in the Additional Resources section.
+
+Haar-cascade Detection in OpenCV
+--------------------------------
+OpenCV provides a training method (see @ref tutorial_traincascade) or pretrained models, that can be read using the @ref cv::CascadeClassifier::load method.
+The pretrained models are located in the data folder in the OpenCV installation or can be found [here](https://github.com/opencv/opencv/tree/3.4/data).
+
+The following code example will use pretrained Haar cascade models to detect faces and eyes in an image.
+First, a @ref cv::CascadeClassifier is created and the necessary XML file is loaded using the @ref cv::CascadeClassifier::load method.
+Afterwards, the detection is done using the @ref cv::CascadeClassifier::detectMultiScale method, which returns boundary rectangles for the detected faces or eyes.
@add_toggle_cpp
This tutorial code's is shown lines below. You can also download it from
@@ -35,9 +110,6 @@ This tutorial code's is shown lines below. You can also download it from
@include samples/python/tutorial_code/objectDetection/cascade_classifier/objectDetection.py
@end_toggle
-Explanation
------------
-
Result
------
diff --git a/doc/py_tutorials/py_objdetect/py_face_detection/images/haar.png b/doc/tutorials/objdetect/cascade_classifier/images/haar.png
similarity index 100%
rename from doc/py_tutorials/py_objdetect/py_face_detection/images/haar.png
rename to doc/tutorials/objdetect/cascade_classifier/images/haar.png
diff --git a/doc/py_tutorials/py_objdetect/py_face_detection/images/haar_features.jpg b/doc/tutorials/objdetect/cascade_classifier/images/haar_features.jpg
similarity index 100%
rename from doc/py_tutorials/py_objdetect/py_face_detection/images/haar_features.jpg
rename to doc/tutorials/objdetect/cascade_classifier/images/haar_features.jpg